{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:39:24Z","timestamp":1776443964194,"version":"3.51.2"},"reference-count":73,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,8,26]],"date-time":"2021-08-26T00:00:00Z","timestamp":1629936000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Secretaria de Ciencia, Tecnolog\u00eda e Innovacion (SECTEI)","award":["20170808"],"award-info":[{"award-number":["20170808"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Facial recognition is fundamental for a wide variety of security systems operating in real-time applications. Recently, several deep neural networks algorithms have been developed to achieve state-of-the-art performance on this task. The present work was conceived due to the need for an efficient and low-cost processing system, so a real-time facial recognition system was proposed using a combination of deep learning algorithms like FaceNet and some traditional classifiers like SVM, KNN, and RF using moderate hardware to operate in an unconstrained environment. Generally, a facial recognition system involves two main tasks: face detection and recognition. The proposed scheme uses the YOLO-Face method for the face detection task which is a high-speed real-time detector based on YOLOv3, while, for the recognition stage, a combination of FaceNet with a supervised learning algorithm, such as the support vector machine (SVM), is proposed for classification. Extensive experiments on unconstrained datasets demonstrate that YOLO-Face provides better performance when the face under an analysis presents partial occlusion and pose variations; besides that, it can detect small faces. The face detector was able to achieve an accuracy of over 89.6% using the Honda\/UCSD dataset which runs at 26 FPS with darknet-53 to VGA-resolution images for classification tasks. The experimental results have demonstrated that the FaceNet+SVM model was able to achieve an accuracy of 99.7% using the LFW dataset. On the same dataset, FaceNet+KNN and FaceNet+RF achieve 99.5% and 85.1%, respectively; on the other hand, the FaceNet was able to achieve 99.6%. Finally, the proposed system provides a recognition accuracy of 99.1% and 49 ms runtime when both the face detection and classifications stages operate together.<\/jats:p>","DOI":"10.3390\/jimaging7090161","type":"journal-article","created":{"date-parts":[[2021,8,26]],"date-time":"2021-08-26T09:27:57Z","timestamp":1629970077000},"page":"161","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Efficient Face Recognition System for Operating in Unconstrained Environments"],"prefix":"10.3390","volume":"7","author":[{"given":"Alejandra Sarahi","family":"Sanchez-Moreno","sequence":"first","affiliation":[{"name":"Secci\u00f3n de Estudios de Posgrado e Investigaci\u00f3n, Instituto Polit\u00e9cnico Nacional, Av. Santa Ana 1000, San Francisco Culhuacan, Mexico City 04440, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0337-5364","authenticated-orcid":false,"given":"Jesus","family":"Olivares-Mercado","sequence":"additional","affiliation":[{"name":"Secci\u00f3n de Estudios de Posgrado e Investigaci\u00f3n, Instituto Polit\u00e9cnico Nacional, Av. Santa Ana 1000, San Francisco Culhuacan, Mexico City 04440, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4867-2717","authenticated-orcid":false,"given":"Aldo","family":"Hernandez-Suarez","sequence":"additional","affiliation":[{"name":"Secci\u00f3n de Estudios de Posgrado e Investigaci\u00f3n, Instituto Polit\u00e9cnico Nacional, Av. Santa Ana 1000, San Francisco Culhuacan, Mexico City 04440, Mexico"}]},{"given":"Karina","family":"Toscano-Medina","sequence":"additional","affiliation":[{"name":"Secci\u00f3n de Estudios de Posgrado e Investigaci\u00f3n, Instituto Polit\u00e9cnico Nacional, Av. Santa Ana 1000, San Francisco Culhuacan, Mexico City 04440, Mexico"}]},{"given":"Gabriel","family":"Sanchez-Perez","sequence":"additional","affiliation":[{"name":"Secci\u00f3n de Estudios de Posgrado e Investigaci\u00f3n, Instituto Polit\u00e9cnico Nacional, Av. Santa Ana 1000, San Francisco Culhuacan, Mexico City 04440, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4945-8314","authenticated-orcid":false,"given":"Gibran","family":"Benitez-Garcia","sequence":"additional","affiliation":[{"name":"Graduate School of Informatics and Engineering, The University of Electro-Communications, Chofu-shi 182-8585, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1112","DOI":"10.1587\/elex.6.1112","article-title":"Improving the eigenphase method for face recognition","volume":"6","author":"Hotta","year":"2009","journal-title":"IEICE Electron. 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